alphaXiv ✓

@askalphaxiv · joined 09 Nov 2023

High fidelity research

53 928Followers

87Following

2 844Posts total

109.3KViews on collected posts

Against accounts of the same size

4 posts from the last 90 days, next to the 10K–100K follower range. reaches fewer people than peers of the same size.

Median views2 682this account2 751median for 10K–100K

Reach, %4.97%this account9.00%median for 10K–100K

Engagement, %1.77%this account1.65%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	2 682	2 751	0.97×
Reach (views ÷ followers)	4.97%	9.00%	0.55×
Engagement rate	1.77%	1.65%	1.07×

Others in this range → Compare with another account → How these benchmarks are built →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

98.2K12 May

3.4K2 Sep

4.1K

1.9K3 Sep

1.6K

Last 5 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.72%12 May

1.84%2 Sep

1.40%

1.70%3 Sep

1.94%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.65%.

What the audience does

Likes50.4%754 in total

Reposts6.8%101 in total

Replies1.4%21 in total

Quotes0.9%13 in total

Bookmarks40.6%607 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Latest posts

“Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement” Coding agents right now struggle to build software continuously over long horizons. This paper wraps existing coding agents in repeated planning, coding, and independent testing loops, ht 1.6K views · 28 likes · 2 reposts · 1 replies 03 Sep 2026 “SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers” Looped Transformers usually gain effective depth by spending extra compute, making it unclear whether looping itself helps. SMELT loops the middle half of an MoE Transformer twice while matching FLOPs, https://t. 1.9K views · 26 likes · 5 reposts · 1 replies 03 Sep 2026 "Losing my religion" A beautifully written blog by professor @togelius on reckoning with the fear that AI will one day make human thought unnecessary. His proposal, complementary intelligence, argues that keeping humans in the loop is not a constraint on progress. Rather, it is 4.1K views · 47 likes · 6 reposts · 3 replies 02 Sep 2026 “Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall” Knowledge distillation helps reasoning during mid-training, but surprisingly hurts factual recall because teachers are much less confident on the remaining knowledge-heavy tokens. So this paper ht 3.4K views · 57 likes · 5 reposts · 1 replies 02 Sep 2026 Reinforcing Recursive Language Models Can a 4B model learn to recursively call itself to answer hard long-context questions? We RL fine-tuned a small model to behave as a native RLM. On evidence selection across scientific papers, our 4B RLM matches Sonnet 4.6 in quality http 98.2K views · 596 likes · 83 reposts · 15 replies 12 May 2026